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[2602.15971] B-DENSE: Branching For Dense Ensemble Network Learning
Summary
The paper presents B-DENSE, a novel framework for improving dense ensemble network learning by leveraging multi-branch trajectory alignment to enhance image generation quality.
Why It Matters
B-DENSE addresses the challenges of high inference latency and loss of structural information in generative models, which are critical for advancing machine learning applications in computer vision. This innovation could lead to more efficient and accurate models, impacting various fields such as AI-generated content and real-time image processing.
Key Takeaways
- B-DENSE improves generative modeling by reducing inference latency.
- The framework utilizes multi-branch trajectory alignment for better training.
- It addresses the loss of structural information in distillation techniques.
- B-DENSE demonstrates superior image generation quality compared to existing methods.
- The approach could enhance applications in AI-generated content and real-time processing.
Computer Science > Machine Learning arXiv:2602.15971 (cs) [Submitted on 17 Feb 2026] Title:B-DENSE: Branching For Dense Ensemble Network Learning Authors:Cherish Puniani, Tushar Kumar, Arnav Bendre, Gaurav Kumar, Shree Singhi View a PDF of the paper titled B-DENSE: Branching For Dense Ensemble Network Learning, by Cherish Puniani and 4 other authors View PDF HTML (experimental) Abstract:Inspired by non-equilibrium thermodynamics, diffusion models have achieved state-of-the-art performance in generative modeling. However, their iterative sampling nature results in high inference latency. While recent distillation techniques accelerate sampling, they discard intermediate trajectory steps. This sparse supervision leads to a loss of structural information and introduces significant discretization errors. To mitigate this, we propose B-DENSE, a novel framework that leverages multi-branch trajectory alignment. We modify the student architecture to output $K$-fold expanded channels, where each subset corresponds to a specific branch representing a discrete intermediate step in the teacher's trajectory. By training these branches to simultaneously map to the entire sequence of the teacher's target timesteps, we enforce dense intermediate trajectory alignment. Consequently, the student model learns to navigate the solution space from the earliest stages of training, demonstrating superior image generation quality compared to baseline distillation frameworks. Comments: Subjects: Mac...
